Scaling plot to stand-level lidar to province in a hierarchical approach to map forest biomass in Nova Scotia.
Bibliographic record
Abstract
This paper presents a study that used lidar transect, plot and wide area polygon sample data collected across Nova Scotia, Canada from 2005 to 2010 to calibrate and extrapolate above ground forest biomass from permanent sample plots (PSPs) to forest stand polygons to the entire Province. The whole tree dry biomass estimate for the total forest resource inventory (FRI) database in Nova Scotia is ~ 373 x 10 6 tonnes ±39%. Where lidar coverage exists, biomass is modelled at the 25 m grid cell resolution, which is a great improvement over the previous ecoregion level estimates, allowing for more effective operational stand management. Given the large spatio-temporal domain of the data sources, one of the major challenges faced in this study was temporal latency between coincident field, lidar and GIS data inputs, which was a significant contributor to the overall level of uncertainty in the result.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".